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https://issues.apache.org/jira/browse/FLINK-1965?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Theodore Vasiloudis updated FLINK-1965:
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Description:
The Orthant-wise Limited Memory QuasiNewton (OWL-QN) is a quasi-Newton
optimization method similar to L-BFGS that can handle L1 regularization.
Implementing this would allow us to obtain sparse solutions while at the same
time having the convergence benefits of a quasi-Newton method, when compared to
stochastic gradient descent.
[Link to
paper|http://research-srv.microsoft.com/en-us/um/people/jfgao/paper/icml07scalable.pdf]
[Link to example
implementation|http://research.microsoft.com/en-us/downloads/b1eb1016-1738-4bd5-83a9-370c9d498a03/]
was:
The Orthant-wise Limited Memory QuasiNewton (OWL-QN) is a quasi-Newton
optimization method similar to L-BFGS that can handle L1 regularization.
Implementing this would allow us to obtain sparse solutions while at the same
time having the convergence benefits of a quasi-Newton method, when compared to
stochastic gradient descent.
[Link to
paper|http://research.microsoft.com/en-us/downloads/b1eb1016-1738-4bd5-83a9-370c9d498a03/]
> Implement the Orthant-wise Limited Memory QuasiNewton optimization algorithm,
> a variant of L-BFGS that handles L1 regularization
> --------------------------------------------------------------------------------------------------------------------------------
>
> Key: FLINK-1965
> URL: https://issues.apache.org/jira/browse/FLINK-1965
> Project: Flink
> Issue Type: Wish
> Components: Machine Learning Library
> Reporter: Theodore Vasiloudis
> Priority: Minor
> Labels: ML
>
> The Orthant-wise Limited Memory QuasiNewton (OWL-QN) is a quasi-Newton
> optimization method similar to L-BFGS that can handle L1 regularization.
> Implementing this would allow us to obtain sparse solutions while at the same
> time having the convergence benefits of a quasi-Newton method, when compared
> to stochastic gradient descent.
> [Link to
> paper|http://research-srv.microsoft.com/en-us/um/people/jfgao/paper/icml07scalable.pdf]
> [Link to example
> implementation|http://research.microsoft.com/en-us/downloads/b1eb1016-1738-4bd5-83a9-370c9d498a03/]
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